Systems and methods for event summarization from data

Abstract

In some aspects, a method includes extracting sentences from data corresponding to documents. Each extracted sentence includes at least one matched pair (a keyword from a first or second keyword set and an entity from an entity set). The method includes ordering the plurality of extracted sentences based on a distance between a respective keyword and a respective entity in each extracted sentence. The method includes identifying a first type and a second type of extracted sentences from the ordered plurality of extracted sentences. Sentences having the first type include keywords of the first keyword set. Sentences having the second type include keywords of the second keyword set. The method includes generating an extracted summary including at least one sentence having the first type and at least one sentence having the second type, intermixed based on a predetermined order rule set. The method includes outputting the extracted summary.

The invention claimed is: 
     
       1. A method for summarizing data, the method comprising:
 performing taxonomy expansion on a first keyword set and data corresponding to one or more documents each comprising text to generate a second keyword set having a greater number of keywords than the first keyword set; 
 extracting a plurality of sentences from the data corresponding to the one or more documents, wherein each extracted sentence includes at least one matched pair including a keyword from the first keyword set or the second keyword set and an entity from an entity set, and wherein each extracted sentence comprises a single sentence or multiple sentences; 
 ordering the plurality of extracted sentences based on a distance between a respective keyword and a respective entity in each extracted sentence of the plurality of extracted sentences; 
 identifying a first type of extracted sentences from the ordered plurality of extracted sentences, wherein extracted sentences having the first type include one or more keywords included in the first keyword set; 
 identifying a second type of extracted sentences from the ordered plurality of extracted sentences, wherein extracted sentences having the second type include one or more keywords included in the second keyword set; 
 generating an extracted summary that includes at least one sentence having the first type and at least one sentence having the second type, wherein the at least one sentence having the first type is intermixed with the at least one sentence having the second type based on a predetermined order rule set; and 
 outputting the extracted summary. 
 
     
     
       2. The method of  claim 1 , wherein the first keyword set comprises a user-generated keyword set, and wherein the second keyword set comprises an expanded keyword set based on the first keyword set. 
     
     
       3. The method of  claim 1 , wherein generating the extracted summary comprises including, in the extracted summary, a first sentence having the second type, followed by a second sentence having the first type, followed by a third sentence having the first type, based on the predetermined order rule set. 
     
     
       4. The method of  claim 1 , wherein generating the extracted summary comprises including, in the extracted summary, a first sentence having the second type, followed by a second sentence having the first type, followed by a third sentence having the second type, based on the predetermined order rule set. 
     
     
       5. The method of  claim 1 , further comprising determining whether to include an additional sentence in the extracted summary based on a determination whether a sum of a length of the extracted summary and a length of the additional sentence is less than or equal to a threshold. 
     
     
       6. The method of  claim 1 , wherein generating wherein performing the taxonomy expansion to generate the second keyword set comprises:
 generating one or more semantic vectors; 
 for each keyword of the first keyword set:
 determining a semantic vector having a highest similarity score to the keyword; and 
 identifying one or more terms of the determined semantic vector as a candidate term; and 
 
 selecting at least one candidate term to be added to the first keyword set to generate the second keyword set. 
 
     
     
       7. The method of  claim 6 , wherein:
 generating the one or more semantic vectors comprises, for each of the one or more documents, generating a corresponding semantic vector based on a skipgram model that utilizes words and subwords from the document; and 
 generating the second keyword set further comprises, for each keyword of the first keyword set, comparing a similarity score of the determined semantic vector having the highest similarity score to a threshold, 
 wherein the semantic vector is used to identify the candidate term based on a determination that the similarity score of the determined semantic vector is greater than or equal to the threshold. 
 
     
     
       8. The method of  claim 1 , wherein the plurality of extracted sentences are ordered based on a number of words between the keywords and the respective entities in the plurality of extracted sentences. 
     
     
       9. The method of  claim 1 , wherein the predetermined order rule set indicates that sentences having the first type and sentences having the second type are to be intermixed in an alternating order for inclusion in summaries. 
     
     
       10. A system comprising:
 a processor; and 
 a memory storing instructions executable by the processor to cause the processor to:
 perform taxonomy expansion on a first keyword set and data corresponding to one or more documents each comprising text to generate a second keyword set having a greater number of keywords than the first keyword set; 
 extract a plurality of sentences from the data corresponding to the one or more documents, wherein each extracted sentence includes at least one matched pair including a keyword from the first keyword set or the second keyword set and an entity from an entity set, and wherein each extracted sentence comprises a single sentence or multiple sentences; 
 order the plurality of extracted sentences based on a distance between a respective keyword and a respective entity in each extracted sentence of the plurality of extracted sentences; 
 identify a first type of extracted sentences from the ordered plurality of extracted sentences, wherein extracted sentences having the first type include one or more keywords included in the first keyword set; 
 identify a second type of extracted sentences from the ordered plurality of extracted sentences, wherein extracted sentences having the second type include one or more keywords included in the second keyword set; 
 extract a summary that includes at least one sentence having the first type and at least one sentence having the second type, wherein the at least one sentence having the first type is intermixed with the at least one sentence having the second type based on a predetermined order rule set; and 
 output the extracted summary. 
 
 
     
     
       11. The system of  claim 10 , further comprising:
 a database coupled to the processor. 
 
     
     
       12. The system of  claim 11 , wherein the database is configured to store the first keyword set, the second keyword set, the entity set, one or more thresholds, one or more extracted sentences, a plurality of matched pairs, one or more extracted summaries, the predetermined order rule set, or a combination thereof. 
     
     
       13. The system of  claim 10 , further comprising:
 an interface configured to enable communication with a data source that stores the data, an electronic device, or a combination thereof. 
 
     
     
       14. A computer-based tool including non-transitory computer readable media having stored thereon computer code which, when executed by a processor, causes a computing device to perform operations comprising:
 performing taxonomy expansion on a first keyword set and data corresponding to one or more documents each comprising text to generate a second keyword set having a greater number of keywords than the first keyword set; 
 extracting a plurality of sentences from the data corresponding to the one or more documents, wherein each extracted sentence includes at least one matched pair including a keyword from the first keyword set or the second keyword set and an entity from an entity set, and wherein each extracted sentence comprises a single sentence or multiple sentences; 
 ordering the plurality of extracted sentences based on a distance between a respective keyword and a respective entity in each extracted sentence of the plurality of extracted sentences; 
 identifying a first type of extracted sentences from the ordered plurality of extracted sentences, wherein extracted sentences having the first type include one or more keywords included in the first keyword set; 
 identifying a second type of extracted sentences from the ordered plurality of extracted sentences, wherein extracted sentences having the second type include one or more keywords included in the second keyword set; 
 generating an extracted summary that includes at least one sentence having the first type and at least one sentence having the second type, wherein the at least one sentence having the first type is intermixed with the at least one sentence having the second type based on a predetermined order rule set; and 
 outputting the extracted summary. 
 
     
     
       15. The computer-based tool of  claim 14 , wherein the operations further comprise:
 generating a second extracted summary that includes at least one sentence having the first type and at least one sentence having the second type, wherein the at least one sentence having the first type is intermixed with the at least one sentence having the second type based on the predetermined order rule set. 
 
     
     
       16. The computer-based tool of  claim 14 , wherein ordering the plurality of extracted sentences is based further on frequencies of respective one or more keywords included in each extracted sentence. 
     
     
       17. The computer-based tool of  claim 14 , wherein the operations further comprise:
 receiving a selection of a first event category of multiple event categories; and 
 retrieving the first keyword set based on the selection of the first event category. 
 
     
     
       18. The computer-based tool of  claim 17 , wherein the multiple event categories comprise cybersecurity, terrorism, legal/non-compliance, or a combination thereof. 
     
     
       19. The computer-based tool of  claim 14 , wherein:
 an extracted sentence of the plurality of extracted sentences comprises the multiple sentences; and 
 the multiple sentences comprise a sentence that includes the at least one matched pair, a sentence that includes the keyword of the at least one matched pair, a sentence preceding the sentence that includes the keyword of the at least one matched pair, a sentence following the sentence with the keyword the at least one matched pair, a sentence that includes the entity of the at least one matched pair, a sentence preceding the sentence that includes the entity of the at least one matched pair, a sentence following the sentence with the entity of the at least one matched pair, or a combination thereof. 
 
     
     
       20. The computer-based tool of  claim 14 , wherein:
 the data is received from a data source that comprises a streaming data source, news data, a database, or a combination thereof; and 
 the entity set indicates an individual, a company, a government, an organization, or a combination thereof.